12
^{OIL & GAS}
Turning volatility
into advantage:

How AI can build resilience and create value across the oil, gas and LNG value chains

^{OIL & GAS}

Turning volatility into advantage: How AI can build resilience and create value across the oil, gas and LNG value chains

On Monday, your trading desk is staring at widening regional spreads and a sudden repricing of flexible freight contracts. On Wednesday, an unplanned outage cascades through your midstream network, forcing you to rethink your nominations. And on Friday, you realize the downstream plan you made two weeks ago is already out of date. Feedstock prices have moved, product demand has shifted, and your maintenance window has tightened.

This isn’t a product of a bad decision. It’s the "new normal" for the global oil, gas and LNG industry. Economic and geopolitical uncertainty are driving fuel price volatility and making forecasting harder. Logistical chokepoints – from canals to conflict zones – can also extend routes, raise costs, and disrupt energy supply chains.

In this environment, where it’s impossible to predict the next move or shock, companies must get much faster at spotting and responding to signals. The only way to achieve this step change in decision-making – and become an adaptive enterprise – is through AI.

From optimizing regularly to
adapting continually

Traditionally, organizations assumed that optimizing would mostly go to plan. But today’s value chain is more like a live system: interdependent, constraint-driven, and sensitive to changes and shocks.

That’s particularly true of LNG, where every part of the chain is tightly connected – from upstream supply and liquefaction reliability to shipping constraints and regas demand. Add to this the ripple effect of global benchmark prices, and the importance of flexible cargoes, and a small disruption in one part of the chain can have a big knock-on effect on the business.

The answer isn't a sharper forecast – it's a better way to decide when the forecast is uncertain and identify trade-offs to mitigate risks or seize immediate market opportunities. No model will reliably call the next shock, so the edge comes from how fast you turn signals into sound decisions – through an AI-enabled system that continuously senses change, tests scenarios, and recommends actions, with forecasts feeding in as one input. This creates an "adaptive enterprise loop" (see Figure 1), in which you optimize continuously and resilience moves from a slogan to an embedded capability.

Figure 1: The adaptive enterprise loop, enabled by AI

AI senses change, tests scenarios and recommends action-continuously

Where AI can add value

AI brings the most value at points where slightly better decisions bring big results. It detects signals humans might miss, calculates trade-offs, and recommends actions – all while respecting your constraints and appetite for risk.

These points exist along the chain:

Upstream: protecting production and costs

In uncertainty, upstream resilience often means preventing avoidable downtime and optimizing production. AI helps by spotting potential failures earlier, scheduling maintenance for optimal times, and continually fine-tuning production so you get the most output for the lowest cost.

Midstream: keeping
a continuous flow

Bottlenecks, storage constraints, and issues with asset integrity can eat away at midstream value. Compression schedules and the availability of terminals to receive or dispatch product are also factors. AI can help by detecting anomalies, predicting and managing disruption, optimizing shipping costs, and factoring all that into scheduling to keep the network moving.

Downstream: protecting margins when demand is shifting

Refineries and chemical plants win when they continually adapt to changing conditions. That means keeping must-run operations going and responding to feedstock economics, while updating product slates and maintenance windows. AI-supported scheduling can help cut the time needed to adjust your operating plan from days to hours.

Figure 2: Optimizing operations end-to-end with AI

Better decisions at every link in the value chain with an LNG overlay tying them together

LNG: the ultimate test of
adaptive optimization

The LNG market is growing steadily, but a sudden shock – like a war breaking out – can drive up demand beyond what the existing infrastructure can meet.

This forces companies to make complex logistical decisions, fast, to avoid overwhelming their facilities. AI can help them make the best use of what’s available by allocating cargoes continuously based on netbacks, constraints, and risk appetite. It can look at congestion, weather risk, and contractual windows to pick the best shipping routes and schedules. It can also use early market signals to anticipate shifts in demand and price. And it can provide a clear view of your total risk exposure – from physical positions to commercial contracts.

How mature is your AI decision-making?

Most companies already use analytics to make decisions in individual functions. But to optimize your operations and gain a competitive edge, you need to do it holistically. That means building an operating model where AI improves, speeds up, and governs the decisions that matter most across your enterprise.

As a first step, you need to assess where your organization is on the maturity ladder (Figure 3).

Figure 3: The maturity ladder: from reporting to closed loop adaptation

In our experience, most organizations describe themselves as adaptive when they’re in fact prescriptive. That means they use predictive models to feed human-authored playbooks, with people still bridging the gap.

These models answer the question: "What’s the best action to take, given today’s limitations?"

Meanwhile, adaptive models recognize that the constraints themselves are moving. So, they adjust their recommendations continuously, closing the loop with controlled automation.

There are five things which distinguish adaptive organizations in your industry. Here is an example of each:

A unified data foundation with clear ownership and quality management. Most LNG operators have data on cargo nominations, AIS vessel telemetry, weather and route risk, JKM-TTF-HH spreads, and terminal slot availability. An adaptive operator pulls it all together so traders can query the data in real time.
An AI model that recommends, not just reports. A refinery replaces its weekly LP with an AI scheduler that assesses margins, hydrogen levels, and tank capacity every four hours. It articulates trade-offs and generates insights. The scheduler then recommends crude slate and unit runs to improve the bottom line.
Scenario simulations and digital twins that test options before committing. Should a general manager stick to current maintenance schedules for the liquefaction train and potentially miss cargo commitments? Or delay maintenance by 10 days and take the reliability risk? A digital twin quantifies the trade-off.
Clarity around which decisions AI makes, who governs the exceptions and how humans stay in the loop. In a daily 30-minute huddle, attendees from commercial, operations and shipping review AI scenarios – deciding on cargo diversions under a set cost threshold and escalating any above it.
Integrated decision-making across the value chain. A Category-3 storm forces a precautionary shut-in of 400 kbpd offshore and three days of midstream pipeline outages – cutting feedstock to refineries and feed gas to an LNG export terminal. Rather than each desk reacting alone, an AI decision layer models the whole position together: it sequences the shut-in and restart for fastest recovery, reroutes flows through alternate pipelines and storage, flags which refineries can bridge on stored crude, and prices the choices for the cargoes it can no longer fill – defer, draw inventory, or swap with a partner. It hands the team a coordinated, costed plan in hours, rather than the days a manual cross-functional scramble would take.

In our experience, most organizations describe themselves as adaptive when they’re in fact prescriptive."

Struggling to secure investment? Focus on measurable business value

To get C-suite buy-in and funding at scale, you need to show that investing in AI will make your organization outperform its competitors – not only mitigate risk.

Tracking the right metrics will be key. Margins should increase from improved netbacks, reduced give-away, and better slate decisions. Reliability and throughput should also increase because you have fewer unplanned disruptions – and recover from them faster.

The same goes for working capital (because less is caught up in inventory) and decision speed, thanks to faster planning cycles and fewer manual overrides. Your risk should be less concentrated, too, with clear triggers for when to hedge. And because AI finds the cleanest route to production, your emissions intensity improves.

Help your organization take a leap
of faith

To really move the needle on the metrics that matter, you'll need a strong, well-managed data foundation that spans the whole chain – including third-party data such as weather forecasts and vessel positions. But don't treat it as a prerequisite to finish before AI can pay off; that's how proofs of concept stall, waiting for a perfect platform that never arrives. Build the foundation use case by use case, letting it widen with each deployment.

Our message is: don’t be the place where proofs of concept come to die. Instead, start small – where uncertainty is hurting your business most and AI offers the greatest gains. That could be in routing LNG cargo and avoiding demurrage, optimizing short-interval scheduling in refineries, maintaining midstream flow during disruption or maintaining critical equipment. Then repeat the same pattern: establish a baseline, run a pilot, embed the solution in your workflow and expand it across assets and the enterprise.

Conclusion

Volatility is the new normal – and, when handled well, the new advantage. The edge won't come from a better forecast but from better decisions when the forecast breaks. Build the AI-powered adaptive loop that senses, tests and acts, and you stop bracing for the next shock and start trading on it – capturing spreads and protecting margins while rivals are still rebuilding last week's plan by hand. That's what turning volatility into advantage looks like.

Authors

Chad Klekar

Vice President, Global O&G Operational Efficiency Lead, Capgemini

Claire Gauthier

Executive Vice President, Global Lead of Energy & Utilities, Capgemini